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Nanomaterials
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AI-Driven Materials Design: Revolutionizing the Future of Nanomaterials
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AI-Driven Materials Design: Revolutionizing the Future of Nanomaterials

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanofabrication and Nanomanufacturing".

Deadline for manuscript submissions: 15 February 2027 | Viewed by 2314

Editors

Dr. Kai Ren

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: nanoscale thermal transport; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ke Wang

E-Mail Website
Guest Editor
School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Interests: electronic engineering; electrical engineering; materials engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of nanomaterials science is undergoing a transformative shift due to the advent of artificial intelligence (AI). Traditionally, nanomaterials design has relied heavily on time-consuming experimental methods and iterative modeling, which often limit the speed and scope of discovery. AI-driven approaches, however, are poised to revolutionize this field by enabling the rapid identification, optimization, and development of materials with tailored properties. These advancements have the potential to significantly impact various industries, including electronics, aerospace, energy, and healthcare. The integration of AI into materials science not only accelerates the discovery process but also allows for more complex and precise designs that were previously unattainable.

While the application of AI in nanomaterials design presents numerous opportunities, several challenges must be addressed. These include the need for large, high-quality datasets to train machine learning models, the integration of AI with existing experimental and computational methods, and the development of algorithms that can accurately predict material behaviors across different scales. Additionally, there are challenges related to the interpretability of AI models and the need for collaboration between AI experts and materials scientists. Despite these challenges, the potential benefits of AI-driven materials design, such as reduced development times, cost savings, and the ability to explore vast chemical spaces, offer substantial opportunities for innovation and advancement in the field.

The primary objective of this Special Topic is to compile and present cutting-edge research that demonstrates the application of AI in materials design. Specifically, the proposal seeks to cover the following:

  • Materials discovery and design: machine learning–driven frameworks for predicting nanomaterials properties, generative models for discovering novel compounds, and accelerated screening strategies for advanced functional nanomaterials.
  • Interpretable and physics-informed artificial intelligence: algorithmic approaches that explicitly incorporate physical and chemical principles, providing mechanistic insights into material behavior and system-level performance.
  • Data infrastructure and reproducibility: development of robust nanomaterials databases, open-source platforms, and standardized workflows to ensure reproducibility and facilitate collaborative innovation.
  • Scalability and real-world deployment: demonstrations and case studies highlighting the translation of data-driven approaches into industrial materials design, energy technologies, and sustainable systems.

Dr. Kai Ren
Dr. Ke Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nanomaterials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • materials informatics
  • machine learning for materials
  • physics-informed artificial intelligence
  • data-driven materials design
  • nanomaterials

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 5298 KB  
Article
Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction
by Qihang Xu, Jian Wu, Xiuying Zhang and Sicong Zhu
Nanomaterials 2026, 16(14), 871; https://doi.org/10.3390/nano16140871 - 15 Jul 2026
Viewed by 253
Abstract
Accurately characterizing the anisotropic optoelectronic properties of crystals requires determining the band gaps at specific high-symmetry points in the Brillouin zone. Relying solely on the minimum band gap is insufficient. However, although Graph Neural Networks (GNNs) offer rapid property predictions, conventional models remain [...] Read more.
Accurately characterizing the anisotropic optoelectronic properties of crystals requires determining the band gaps at specific high-symmetry points in the Brillouin zone. Relying solely on the minimum band gap is insufficient. However, although Graph Neural Networks (GNNs) offer rapid property predictions, conventional models remain trapped in a local real-space paradigm, lacking the global symmetry information necessary to differentiate these high-symmetry energy states. To address this problem, we propose the Symmetry-Information-Enhanced Crystal Graph Neural Network (Symm-CGNN). It explicitly incorporates local atomic environments with global symmetry information, including space groups, crystal systems, material density and lattice constants. The evaluation is performed on a comprehensive dataset including 3D (Materials Project) and 2D (2DMatpedia). The results demonstrate that Symm-CGNN achieves an 18% reduction in Mean Absolute Error (MAE) for high-symmetry band gap prediction compared to the baseline Crystal Graph Convolutional Neural Networks (CGCNN). This approach bridges the representational gap between local atomic coordination and macroscopic symmetry. Consequently, it provides a robust and efficient machine-learning paradigm for the high-throughput screening of materials with anisotropic optoelectronic properties. Full article
(This article belongs to the Special Issue AI-Driven Materials Design: Revolutionizing the Future of Nanomaterials)
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19 pages, 1417 KB  
Article
AI-Driven Design and Comparative Evaluation of SNEDDS for the Optimized Nanoencapsulation of Phytoextracts
by Cassandra G. Prieto-Medrano, Gildardo Sanchez-Ante, Araceli Zavala, Angélica Lizeth Sánchez-López, Adriana Cavazos-Garduño, Ana Karina Carrillo-Pérez, Rebeca Garcia-Varela and Yocanxóchitl Perfecto-Avalos
Nanomaterials 2026, 16(13), 793; https://doi.org/10.3390/nano16130793 - 26 Jun 2026
Viewed by 1099
Abstract
Oil-in-water nanoemulsions (NE) can increase the water solubility of plant-derived bioactive molecules as drug candidates. Machine learning-guided NE design can prevent the expensive, time-consuming trial-and-error process. NE composition data was aggregated into a dataset; a predictive machine learning model identified improved self-nanoemulsifying system [...] Read more.
Oil-in-water nanoemulsions (NE) can increase the water solubility of plant-derived bioactive molecules as drug candidates. Machine learning-guided NE design can prevent the expensive, time-consuming trial-and-error process. NE composition data was aggregated into a dataset; a predictive machine learning model identified improved self-nanoemulsifying system formulations (olive oil and combinations of Tween 20, Tween 80, glycerol, and soy lecithin). Predictive power was assessed by estimating successful self-nanoemulsification through transmittance and Dynamic Light Scattering. NEs were loaded with an organic extract containing anacardic acid. Encapsulation efficiency was measured by UHPLC. Antiproliferative activity was evaluated on human hepatic cancer (Hep G2) and normal-like human embryonic kidney (HEK-293) cell lines. The model showed an accuracy of 81%. The best-performing formulation, consisting of 10% olive oil, 60% Tween 20, and 30% glycerol, exhibited an average particle size of 162.8 ± 26 nm, a polydispersity index of 0.234 ± 0.03, and high encapsulation efficiency. While HEK-293 cells remained unaffected, naked NE exhibited a selective growth inhibitory effect on the Hep G2 cell line. Loaded NE increased the cytotoxic effect on Hep G2 (IC50: 5.9 ± 1.27 µM). Machine learning-guided NE formulation was a successful carrier for the plant extract and the molecule of interest, providing a proof of concept for how artificial intelligence can shorten the development pipeline for NE drug delivery systems. Full article
(This article belongs to the Special Issue AI-Driven Materials Design: Revolutionizing the Future of Nanomaterials)
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26 pages, 6313 KB  
Article
Optimization of Mechanical Properties of Eco-Friendly Mortar Containing Wood Ash and Nano Silica Using Response Surface Methodology and Artificial Neural Networks
by Abiodun Akinwale, Walied A. Elsaigh and Akeem Ayinde Raheem
Nanomaterials 2026, 16(12), 717; https://doi.org/10.3390/nano16120717 - 10 Jun 2026
Viewed by 546
Abstract
As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and [...] Read more.
As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL−1) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R2 = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article
(This article belongs to the Special Issue AI-Driven Materials Design: Revolutionizing the Future of Nanomaterials)
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